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BackOps is building the AI resolution layer for companies in the physical economy—logistics, manufacturing, retail, and related industries. The company's platform, Relay, deploys AI operators that automate operational workflows across fragmented enterprise systems (ERPs, WMSs, TMSs, carrier portals, email, spreadsheets, legacy systems). Rather than building dashboards or copilots, BackOps is creating an execution layer that understands workflows, takes action across systems, navigates exceptions, and carries work through to resolution. The company recently raised a $42M Series B led by Insight Partners.
As an AI/ML Engineer, you will own the infrastructure and systems that make Relay's underlying intelligence more capable, efficient, and reliable. You'll work at the intersection of machine learning, distributed systems, and production AI, designing infrastructure that supports model execution, inference optimization, evaluation, and continuous improvement.
Key responsibilities include:
• Build the infrastructure behind Relay's intelligence: Own the architecture and engineering of AI/ML systems powering Relay, including model serving, inference pipelines, execution infrastructure, model selection and routing, shared interfaces for foundation models, infrastructure for experimentation and evaluation, model versioning and lifecycle management, and compute orchestration.
• Make intelligence faster, more efficient, and more scalable: Optimize infrastructure and execution patterns to meet performance and reliability constraints of real enterprise workflows. Identify bottlenecks across the AI/ML stack, evaluate architectural tradeoffs, and implement improvements that support increasingly sophisticated workloads while maintaining quality and reliability. Make better intelligence economically viable at scale.
• Continuously improve model intelligence and quality: Develop systems and technical approaches for systematic model quality improvement, including model evaluation and benchmarking, model selection and routing, context engineering and retrieval, fine-tuning and model adaptation, and experimentation frameworks connecting model changes to production improvements.
• Engineer reliability into production AI: Build infrastructure for observability, failure detection, recovery mechanisms, performance monitoring, and operational controls. Establish engineering standards for safely introducing new models, evaluating changes, managing dependencies, and operating complex AI systems in production.
• Make AI/ML performance measurable: Establish technical metrics and feedback loops including model accuracy, inference latency and throughput, cost per inference, model reliability and error rates, evaluation performance, resource utilization, and deployment time. Connect technical improvements to business outcomes: more accurate decisions, reliable execution, and greater autonomous resolution.
This is a senior individual contributor role with significant leverage. You'll help define the AI/ML foundation BackOps scales on, working closely with the CTO and engineering teams. This is not a role focused on integrating AI APIs or maintaining an established ML stack—you'll make foundational technical decisions and build capabilities from the ground up.
REQUIREMENTS:
• 6+ years of relevant software engineering experience with meaningful ownership of complex technical systems at Senior or Staff level
• Built, deployed, or optimized production AI/ML infrastructure, model-serving systems, inference pipelines, or similarly sophisticated AI systems
• Exceptional engineering fundamentals and strong proficiency in Python
• Experience designing and operating reliable distributed systems
• Understanding of modern foundation models and tradeoffs in model selection, inference architecture, quality, latency, throughput, scalability, and cost
• Hands-on experience with cloud infrastructure, containerization, orchestration, and operational challenges of running production AI workloads
• Understanding of model evaluation and experimentation, with ability to connect technical changes to measurable improvements in model quality or system performance
• Ability to move fluidly between foundational architecture, hands-on implementation, performance debugging, and production operations
• Experience in environments where technical requirements evolved rapidly and architectural decisions balanced immediate needs with long-term scalability
• Strong opinions, loosely held; ability to create clarity with incomplete information, explain technical tradeoffs, and adapt quickly
• High bar for engineering quality while moving with urgency
PARTICULARLY COMPELLING:
• Built foundational AI/ML infrastructure at a breakout AI company or exceptional technology organization where performance, reliability, and scale were critical
• Deep experience with PyTorch, Hugging Face, vLLM, Triton, Ray, Kubernetes, or comparable model-serving and distributed-computing frameworks
• Designed systems for model optimization, fine-tuning, post-training, evaluation, or continuous improvement of production AI capabilities
• Built infrastructure supporting agentic AI systems, complex model orchestration, or multi-step autonomous workflows
• Delivered measurable improvements in model quality, inference efficiency, infrastructure cost, or production reliability
• Built shared technical capabilities from zero, establishing architectures and engineering standards for other teams
• Experience with supply chain or logistics (useful but not required; exceptional engineering judgment and AI/ML systems depth matter more)
